Impact of decision theoretic models in information elicitation

预测市场和评分规则每天都被用来得出个人对未来事件结果的概率估计。例如，“B项目是否会在2018年6月1日前完成？”是一种可以在预测市场交易的证券，或者让代理人在评分规则中报告他们的信念。预测市场和评分规则用于没有历史数据的问题和实例，预测未来事件结果的唯一方法是询问专家、工作人员或任何有意见的人，从而利用“人群的智慧”。*最初提出时，预测市场和评分规则都被证明对风险中性的代理人(在概率为0.5的情况下获得50美元或100美元之间无关紧要，否则什么都不是)、短视的(不考虑未来的收益)和理性的(最大化其总回报)都有效。众所周知，风险中立、理性和短视行为在实践中是不成立的，已经提出了更现实的人类决策模型，并在各种实验室和现场研究中得到了验证。在拟议的研究计划中，我们将表征预测市场和评分规则的有效性，当使用这些更现实和现代的决策模型对人类和代理进行建模时。特别是，我们将表征当参与者是累积前景理论代理人时，当外部激励存在时，当参与者是模糊厌恶代理人时，以及当代理人具有双曲贴现时，预测市场和评分规则收集信息的情况。所有这些模型都已被证明在实践中存在于人类身上，但它们没有在预测市场中被考虑，其中一些还没有在评分规则中被考虑。这个研究项目的结果将是新的预测市场和评分规则机制，当面对使用上面讨论的决策模型建模的代理时，这些机制提高了这些概率启发方法的准确性。*拟议研究计划的结果将是来自预测市场的更准确的预测和评分规则，这些规则在实践中比提出这些机制时考虑的环境中出现得更频繁。这项研究将进一步突出预测市场和评分规则的局限性和好处。此外，还可以为所考虑的环境开发新的信息获取机制。加拿大将从拟议的研究中受益，因为我们将努力使开发的方法在可能部署在加拿大组织中的软件中可用，从而为它们提供独特的竞争优势，即对未来事件的可能性有更准确的估计，从而导致更快和更准确的决定。根据开发的计划培训的HQP可以部署开发的机制，或者在整个加拿大的工业、教育或公共部门使用他们的分析和技术技能。

Prediction markets and scoring rules are used daily to elicit personal probability estimates on the outcome of future events. For example, “Will project B be complete by June 1, 2018?” is one security that may be traded in a prediction market, or have agents report their beliefs in a scoring rule. Prediction markets and scoring rules are used for questions and instances in which there is no historical data, and the only way to forecast the outcome of a future event is to ask experts, workers, or anyone having an opinion, thereby leveraging the “wisdom of the crowd.”******When initially proposed, both prediction markets and scoring rules were shown to work for agents that are risk-neutral (are indifferent between getting $50 or $100 with probability 0.5, and nothing otherwise), myopic (do not take future payoffs into account), and rational (are maximizing their total reward). Risk-neutrality, rationality, and myopic behavior are known not to hold in practice, and more realistic models of human decision-making have been proposed and verified in various laboratory and field studies. In the proposed research program, we will characterize the efficacy of prediction markets and scoring rules when humans, agents, are modeled using these more realistic and modern decision models. In particular, we will characterize how well prediction markets and scoring rules aggregate information when participants are cumulative prospect theory agents, when external incentives exist, when participants are ambiguity averse agents, and when agents have hyperbolic discounting. All of these models have been shown to exist in humans in practice, but they have not been considered in prediction markets, and some have not been considered in scoring rules. The results of this research program will be new prediction market and scoring rule mechanisms that improve the accuracy of these probability elicitation methods when faced with agents modeled using the decision models discussed above. ******The outcomes of the proposed research program will be more accurate forecasts from prediction markets and scoring rules in settings that occur more often in practice than the settings considered when these mechanisms were proposed. This research will further highlight the limitations and benefits of prediction markets and scoring rules. In addition, new information elicitation mechanisms may be developed for the settings considered. Canada stands to benefit from the proposed research as we will work to make the developed methods available in software that may be deployed in Canadian organizations, thereby providing them with a distinct competitive advantage of having more accurate estimates on the likelihood of a future event, leading to faster and more accurate decisions. HQP trained under the developed program may deploy the developed mechanisms or use their analysis and technical skills in industry, education, or public sector throughout Canada.